How AI in product analytics unlocks deeper insights, faster (+ examples)

To build a product that users love—and return to again and again—you need to deeply understand their needs, jobs to be done, and pain points across multiple use cases and devices. But capturing, analyzing, and synthesizing all the datapoints required to do this effectively can quickly overwhelm product managers and their teams—they end up with lots of information but no time to turn it into the insights they actually need.

AI in product analytics (PA) transforms your complex data into actionable next steps. Using AI models, machine learning, and generative AI (genAI), modern PA tools quickly analyze reams of quantitative and qualitative data to provide instant insights and data-driven guidance. 

This article shows you 5 ways using AI for product analytics helps you make faster, smarter product decisions based on real user needs.

Key insights

• Modern tools are redefining analytics with AI, removing the need for complex and time-consuming training or input from data scientists or analysts—so you can democratize your data, get actionable insights, and iterate quickly

• An AI-powered product analytics platform connects the dots between multiple touchpoints in the user’s product journey, breaking down silos so teams don’t have to switch between separate analytics tools

• Incorporate evidence from product analytics—like AI-generated summaries, video clips of user behavior, and impact evaluations—to bring your product roadmap to life and make a persuasive case for new features and improvements to stakeholders

1. Effortlessly analyze complex data with an AI copilot

Your product is a goldmine of user behavior data, but connecting the dots between each data source to find correlations, trends, and opportunities is impossible for your time-strapped product team alone. 

Use an AI copilot for data analysis—like Contentsquare’s Chat with Sense—to ask your product analytics platform questions about your tool, features, or app in natural language.

Sense automatically detects the best way to analyze your data, translating your request into the right analysis and events. It also provides shareable visualizations like charts and graphs, explains how it reached its conclusions, and suggests follow-up questions to help you dig even deeper into your data.

Ask questions related to key product analytics metrics, like activation, product usage, engagement, retention, and churn. For example:

• How do monthly active users (MAU) compare between this month and last month? Why has this increased or decreased?

• Which user segment is most at risk of churn?

• Where do users drop off in the onboarding process and why?

• How does feature usage vary across desktop and mobile?

Then, use these AI analytics insights to optimize your product and address user needs, such as by

• Triggering activation campaigns to bring disengaged users back to your product by highlighting features relevant to their use cases

• Creating new content marketing materials or running webinars about common roadblocks to support onboarding and activation

• Making improvements to your mobile user experience (UX) to address common usability issues

2. Discover which product pathways lead to success with AI-powered journey analysis

To understand how people really use your product, you need to see the full picture: not just individual events, like the flashes of frustration or eventual conversions, but the entire customer journey leading up to those points—and even beyond.

Contentsquare’s AI-powered Journey Analysis reveals how users progress through your product from entry to exit, including where they engage and where they get stuck or bounce.

Use Sense to quickly dig into your journey data to understand

• Common paths taken by your most engaged users

• How different user segments explore your product

• Journeys or workflows that result in drop-offs or churn, likely due to underlying frustration

Then, use these data-driven insights to improve how users navigate your product by

• Creating tailored onboarding journeys for different audiences to boost stickiness and feature adoption

• Addressing pain points in specific journeys that impact engagement and retention

• Encouraging more users to take the most effective path through your product to help them reach their ‘aha!’ moments faster

Use AI-powered Journey Analysis in Contentsquare to discover common paths users take in your product

3. Watch how users engage with features using AI-generated session replay summaries

Session replays are a powerful tool to discover how users interact with your product and features, but until now, they’ve been time-consuming for product teams to watch at the scale required to get real, needle-moving insights.

Contentsquare’s AI-powered Session Replay Summaries feature combines hours of recordings to provide you with an immediate summary of key takeaways, potential issues, and behavioral trends—all with time-stamped clips that let you jump right to the most crucial moments.

Session Replay Summaries analyzes hours of footage to compile key user behavior insights

Use Contentsquare’s AI-powered Session Replay Summaries to

• Watch how users engage with newly launched features and spot any functionality or UX issues that impact activation or adoption

• Get additional context for user journeys, bounces, and drop-offs in your product by zooming in on the exact moments they happen

• See how product experiences differ by parameters like user segment, location, or device type to understand different customer needs

Then, use these AI-generated insights to make user-centric product improvements, like

• A/B testing microcopy or designs to make new features more appealing or easier to use (and watching more session replays from your experiment to understand why your winning variant succeeds)

• Fixing hard-to-replicate bugs or technical errors impacting specific groups of users

• Identifying and empathizing with the emotions behind user friction to effectively address them (for example, if the summaries reveal that users are rage clicking on an element and suggests this means they’re looking for more information, you can add more details to address this underlying need)

💡 Pro tip: filter sessions using Contentsquare’s frustration score to understand where and why users experience frustration, and use Session Replay Summaries to get recaps of one or multiple frustrating sessions. 

The AI-based frustration score considers a range of UX, technical, and performance issues to assign sessions or pages a score from 0 to 100. The clear, objective ranking system highlights which areas need immediate attention, improving prioritization and empowering you to reduce the most impactful causes of user frustration in your product, faster. 

Want a real-time pulse check? Add a frustration score widget to your product analytics dashboard in Contentsquare to get an at-a-glance visualization of user frustration and easily monitor this metric over time.

4. Streamline user research for product development with AI-driven surveys

The insights you glean from watching what users do (and don’t do) in your product are invaluable. But to truly understand their motivations and requirements, you also need to hear from them directly.

Incorporate qualitative research into your product analytics strategy with AI-driven surveys. Get user feedback on existing or future features and products and use the results to inform decision-making.

Let Contentsquare Sense generate questions based on your research goal and get a ready-to-launch survey in minutes. For example, you might want to learn

• Why aren’t users adopting our newest feature?

• What features do our ideal customers want to see next?

• Why don’t users convert after their free trial?

Once results start coming in, use our AI to create a Summary Report from survey results that includes main focus areas, relevant quotes, and suggested next steps—all without requiring your team to manually review every response. Get even deeper insights by using AI to assign custom tags (such as ‘feature request’, ‘UX’, or ‘bug’) and analyze sentiment by tagging responses as positive, neutral, or negative to give you more detailed breakdowns.

Use your findings to

• Prioritize your short-term and long-term roadmap based on the high-priority customer needs and allocate technical resources accordingly

• Get executive buy-in for features, improvements, or initiatives by supporting them with voice-of-customer evidence

• Enrich your data by connecting survey responses to associated session replays in Contentsquare to see what happened before and after users left their feedback, offering additional context for why they answered the way they did

AI-powered sentiment analysis gives you instant insights without the effort

💡 Pro tip: received a particularly intriguing piece of feedback? Invite the respondent to follow up with Contentsquare Interviews. Automate the admin by scheduling, hosting, and transcribing your interview calls all in our platform, then leverage AI in user interviews to streamline analysis and uncover key takeaways.

5. Connect product improvements to business impact  

When you need to make fast decisions about where to allocate your resources and what to prioritize, it can be tempting to listen to the loudest customer requests or your CEO’s gut instinct. 

Instead, use AI tools for advanced analytics like Contentsquare to measure the impact of your product experience on business outcomes, such as conversions and revenue, and make informed decisions. 

Sense draws on Contentsquare’s Impact Quantification capability to translate events in your product into real numbers, so you know which issues need urgent attention and which opportunities you should capitalize on ASAP.

To understand the impact of recent product changes, UX improvements, or new features, ask Sense questions like

• Which errors had the biggest impact on conversion?

• How did different user segments contribute to conversion goals?

• What’s the projected revenue gain (or loss) if this behavior continues?  

Then, use Sense’s evaluations to

• Get buy-in for UX improvements or initiatives with a high return on investment (ROI)

• Mobilize cross-functionally to address the most damaging bugs or errors before they affect your bottom line

• Set realistic benchmarks that you can use to prove product impact going forward

Enhance your product analytics with AI-powered insights

All the data in the world won’t help you build a successful product if you don’t take the time to analyze it and put your findings into action. That’s why you need AI in product analytics: a trusted, built-in data analyst that does the heavy lifting to uncover issues and opportunities, accelerating time to insight so you and your team can make data-driven decisions and focus on what really matters—delighting your users.

FAQs about AI in product analytics